Configure your deployment
You can deploy your impulse to any device. This makes the model run without an internet connection, minimizes latency,
and runs with minimal power consumption.
Read more.
Search deployment options
No deployment options available for this project.
Deploy to any Linux-based development board
Edge Impulse for Linux lets you run your models on any Linux-based development board,
with SDKs for Node.js, Python, Go and C++ to integrate your models quickly into
your application.
- Install the Edge Impulse Linux CLI
- Run
edge-impulse-linux-runner(run with--cleanto switch projects)
See the documentation for more information and setup instructions.
Alternatively, you can download your model for below.
Run your model as a Docker container
To run your model as a container with an HTTP interface, use:
Container:
public.ecr.aws/z9b3d4t5/inference-container:f7506662b79167fb1e67701d333af8feb04ff22e
Arguments:
--api-key ei_d58558699dab88d38983d08be7ccead54f157b2df626bf8418e2610141503f9f --run-http-server 1337
Ports to expose:
1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/z9b3d4t5/inference-container:f7506662b79167fb1e67701d333af8feb04ff22e \
--api-key ei_d58558699dab88d38983d08be7ccead54f157b2df626bf8418e2610141503f9f \
--run-http-server 1337
This automatically builds and downloads the latest model (incl. hardware optimizations), and runs an HTTP endpoint at http://localhost:1337 with instructions.
Read the docs for information,
like bundling in your model inside the container and selecting extra hardware optimizations.
Run your model as a Docker container
To run your model as a container with an HTTP interface on NVIDIA Jetson's GPUs (JetPack 4.6.x), use:
Container:
public.ecr.aws/z9b3d4t5/inference-container-jetson:5bdd14a7e1811111c8e01c64947fc045f576399d
Arguments:
--api-key ei_d58558699dab88d38983d08be7ccead54f157b2df626bf8418e2610141503f9f --run-http-server 1337
Ports to expose:
1337
For example, in a one-liner locally:
docker run --rm -it --runtime=nvidia --gpus all \
-p 1337:1337 \
public.ecr.aws/z9b3d4t5/inference-container-jetson:5bdd14a7e1811111c8e01c64947fc045f576399d \
--api-key ei_d58558699dab88d38983d08be7ccead54f157b2df626bf8418e2610141503f9f \
--run-http-server 1337
This automatically builds and downloads the latest model (incl. hardware optimizations), and runs an HTTP endpoint at http://localhost:1337 with instructions.
Read the docs for information,
like bundling in your model inside the container and selecting extra hardware optimizations.
Run your model as a Docker container
To run your model as a container with an HTTP interface on NVIDIA Jetson Orin's GPUs (JetPack 5.1.x), use:
Container:
public.ecr.aws/z9b3d4t5/inference-container-jetson-orin:7527dbccfd89c3e8c1d2785aa60d2dfc2bfd53dd
Arguments:
--api-key ei_d58558699dab88d38983d08be7ccead54f157b2df626bf8418e2610141503f9f --run-http-server 1337
Ports to expose:
1337
For example, in a one-liner locally:
docker run --rm -it --runtime=nvidia --gpus all \
-p 1337:1337 \
public.ecr.aws/z9b3d4t5/inference-container-jetson-orin:7527dbccfd89c3e8c1d2785aa60d2dfc2bfd53dd \
--api-key ei_d58558699dab88d38983d08be7ccead54f157b2df626bf8418e2610141503f9f \
--run-http-server 1337
This automatically builds and downloads the latest model (incl. hardware optimizations), and runs an HTTP endpoint at http://localhost:1337 with instructions.
Read the docs for information,
like bundling in your model inside the container and selecting extra hardware optimizations.
Run your model as a Docker container
To run your model as a container with an HTTP interface on NVIDIA Jetson Orin's GPUs (JetPack 6.0), use:
Container:
public.ecr.aws/z9b3d4t5/inference-container-jetson-orin-6-0:57b6946617cad3b76737f7a0b1d1baecc9856110
Arguments:
--api-key ei_d58558699dab88d38983d08be7ccead54f157b2df626bf8418e2610141503f9f --run-http-server 1337
Ports to expose:
1337
For example, in a one-liner locally:
docker run --rm -it --runtime=nvidia --gpus all \
-p 1337:1337 \
public.ecr.aws/z9b3d4t5/inference-container-jetson-orin-6-0:57b6946617cad3b76737f7a0b1d1baecc9856110 \
--api-key ei_d58558699dab88d38983d08be7ccead54f157b2df626bf8418e2610141503f9f \
--run-http-server 1337
This automatically builds and downloads the latest model (incl. hardware optimizations), and runs an HTTP endpoint at http://localhost:1337 with instructions.
Read the docs for information,
like bundling in your model inside the container and selecting extra hardware optimizations.
Run your model as a Docker container
To run your model as a container with an HTTP interface on Qualcomm Adreno 702 GPUs, use:
Container:
public.ecr.aws/z9b3d4t5/inference-container-qc-adreno-702:4496c16e5c16577e7b00252a4f6d7020ae63a67e
Arguments:
--api-key ei_d58558699dab88d38983d08be7ccead54f157b2df626bf8418e2610141503f9f --run-http-server 1337
Ports to expose:
1337
For example, in a one-liner locally:
docker run --rm -it --device /dev/dri \
-p 1337:1337 \
public.ecr.aws/z9b3d4t5/inference-container-qc-adreno-702:4496c16e5c16577e7b00252a4f6d7020ae63a67e \
--api-key ei_d58558699dab88d38983d08be7ccead54f157b2df626bf8418e2610141503f9f \
--run-http-server 1337
This automatically builds and downloads the latest model (incl. hardware optimizations), and runs an HTTP endpoint at http://localhost:1337 with instructions.
Read the docs for information,
like bundling in your model inside the container and selecting extra hardware optimizations.
Clone this project to deploy this impulse.
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